P511: INTEGRATION OF TRANSCRIPTOMIC FEATURES IMPROVES PROGNOSIS PREDICTION OF PEDIATRIC ACUTE MYELOID LEUKEMIA WITH KMT2A REARRANGEMENTS
Authors: Jun LI, Suyu Zong, Yang Wan, Ruan Min, Wenyu Yang, LI Zhang, Xiaojuan Chen, Ye Guo, Yingchi Zhang, Xiaofan Zhu
Journal: HemaSphere (2023)

Background:KMT2A-rearranged acute myeloid leukemia (KMT2A-r AML) is a special entity in the 2022 World Health Organization classification of myeloid neoplasms, characterized by high relapse rate and dismal prognosis. Till now, no effective prognostic factor had been identified for pediatric KMT2A-r AML. A more effective prognostic system is in urgent need. Aims: To study the transcriptomic feature of pediatric KMT2A-r AML and develop a prognostic model to refine the current risk stratification and provide evidence for clinical decision-making. Methods: Clinical and RNA-seq data of pediatric AML patients were collected from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) program (available at https://ocg.cancer.gov/programs/target/ data-matrix). We included all KMT2A-r AML diagnosed between 1997 and 2016. Only genes that were expressed (RPKM > 1) in more than 30% of samples were further analyzed. Univariable Cox regression was applied to identify the potential genes that are associated with event-free survival. The LASSO algorithm was performed to construct a predictive model based on gene expression. Multivariable Cox regression was applied to validate the independent predictive value of factors. Results: A total of 351 pediatric KMT2A-r AML patients were included and divided into training (n = 229) and validation sets (n = 122) according to their treatment protocols. Univariable Cox analysis was applied to identify candidate genes of which the expression level is associated with EFS in the training set. There were 2043 genes with p value < 0.05 entering LASSO algorithm to construct the prognostic model (pKMT2A7 score) which was finalized with 7 genes. The training set was stratified into 2 groups, G1 (n = 110) and G2 (n = 119), with distinct prognosis. The G1 patients had pKMT2A7 score less than 1.108 with higher 5-year OS (69.6% ± 4.7% vs. 38.4% ± 4.6%, p < 0.001) and EFS (51.7% ± 4.8% vs. 16.1% ± 3.4%, p < 0.001) rates than G2 patients. The patients in the validation set were also separated into G1 (n = 66) and G2 (n = 56) groups. G1 group had better prognosis than G2 group with higher 5-year OS (64.6% ± 6.0% vs. 43.4% ± 7.2%, p = 0.024) and EFS (43.5% ± 6.2% vs. 22.4% ± 5.7%, p = 0.014) rates. To investigate the biological differences, we analyzed the DEGs of the two groups. A total of 1127 up-regulated and 1219 down-regulated genes were finally identified in G2. The top enriched GO terms for up-regulated genes were mostly related to nucleotide metabolic and cell activation, while the most significant pathways included DNA replication, cell cycle and oxidative phosphorylation. In terms of clinical features, G1 group was characterized by younger age (p = 0.002) and higher proportion of KMT2A::MLLT3 fusion gene (p = 0.03). We included pKMT2A7 score into uni- and multivariable Cox analysis. It shows that high pKMT2A7 score is an independent risk factor [HROS = 2.32 (95%CI 1.67 - 3.23), p < 0.001; HREFS = 2.06 (95%CI 1.54 - 2.76), p < 0.001] together with age at diagnosis [HROS = 1.81 (95%CI 1.27 - 2.59), p = 0.001; HREFS = 1.45 (95%CI 1.07 - 1.97), p = 0.017] and translocation partner [HROS = 1.55 (95%CI 1.04 - 2.30), p = 0.032; HREFS = 1.43 (95%CI 1.04 - 1.96), p = 0.027] for both OS and EFS. We then incorporated independent risk factors for EFS into the prognostic model. Using the new prognostic model, patients were stratified into 3 risk groups, low risk (LR), intermediate risk (IR) and high risk (HR). The 5-year OS rates of LR, IR and HR groups were 79.8% ± 5.8%, 60.7% ± 4.0% and 35.0% ± 4.6%, respectively (p < 0.001). The 5-year EFS rates of LR, IR and HR groups were 58.8% ± 6.9%, 40.2% ± 4.0% and 16.0% ± 3.1%, respectively (p < 0.001). Summary/Conclusion: Pediatric KMT2A-r AML is a heterogenous diseases on transcriptomic level and incorporating transcriptomic features can improve prognosis prediction. The newly proposed risk stratification showed robust predictive ability in distinguishing patients with different risk, which can be of great help in clinical routines. Also, our findings provided foundation for further pathophysiology research of pediatric KMT2A-r AML.Keywords: Risk-adapted, Pediatric, AML, 11q23